TEQUILA: a platform for rapid development of quantum algorithms

Abstract

Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods in quantum algorithmic development yet, and the field continues to evolve rapidly. As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in a high demand for flexible and reliable ways to implement, test, and share new ideas. Inspired by this demand, we introduce tequila, a development package for quantum algorithms in python, designed for fast and flexible implementation, prototyping and deployment of novel quantum algorithms in electronic structure and other fields. tequila operates with abstract expectation values which can be combined, transformed, differentiated, and optimized. On evaluation, the abstract data structures are compiled to run on state of the art quantum simulators or interfaces.

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

“…We will start with generalized operators – that don't have to be fermionic excitations – and afterwards show how those operators can be constructed in the fermionic representation. For the convenience of the reader, we summarize our findings in Table 1 and illustrate the implementation of the automatic differentiation within Tequila 36 in Fig. 1 .…”

“…Using an automatically differentiable framework, as for example offered in Tequila, 36 this gradient evaluation procedure can be implemented in a straightforward way. Using the most straightforward realization of this scheme, the explicit implementation of the unitary that is being differentiated will require approximately twice the number of native quantum gates as in the original expectation value since the two generators G ± decompose into a similar number of Pauli strings as the original generator G .…”

“…Our aim is rather to provide a generalized toolbox for automatic differentiable unitary coupled-cluster, employable for the development of new methods. Our implementation is available within the free to use and open-source Tequila 36 package. The improved gradient evaluation schemes are automatically applied to already implemented methods like UpCCGSD 54 that was employed in previously developed basis-set-free methods 55 and as an application for the meta-VQE 56 approach.…”

“…The techniques developed within this work are implemented in the free and open-source package Tequila 36 that operates on abstract expectation values of quantum circuits and operators, which can themselves be transformed and combined in an intuitive black-board style way. Our implementation is inspired by various open-source packages such as MADNESS, 57 PennyLane, 25 and DiffiQult, 26 that are not focused on unitary coupled-cluster, but offer intuitive application programming interfaces, exposing, often highly specialized, numerical algorithms to a broad audience of interested scientists.…”

“…The main results are summarized in Table 1 . All developed techniques are implemented in the open-source package Tequila 36 and allow blackboard style construction of automatically differentiable objectives constructed from fermionic excitation operators and other quantum gates. We present explicit code examples and initial application for electronic ground and excited states.…”